Synopsis
With the resurgence of neural networks in the 2010s, deep learning has become essential for machine learning practitioners and even many software engineers. This book provides a comprehensive introduction for data scientists and software engineers with machine learning experience. Youâ ll start with deep learning basics and move quickly to the details of important advanced architectures, implementing everything from scratch along the way.
Author Seth Weidman shows you how neural networks work using a first principles approach. Youâ ll learn how to apply multilayer neural networks, convolutional neural networks, and recurrent neural networks from the ground up. With a thorough understanding of how neural networks work mathematically, computationally, and conceptually, youâ ll be set up for success on all future deep learning projects.
This book provides:
- Extremely clear and thorough mental modelsâ accompanied by working code examples and mathematical explanationsâ for understanding neural networks
- Methods for implementing multilayer neural networks from scratch, using an easy-to-understand object-oriented framework
- Working implementations and clear-cut explanations of convolutional and recurrent neural networks
- Implementation of these neural network concepts using the popular PyTorch framework
"synopsis" may belong to another edition of this title.
About the Author
Seth is a data scientist who lives in San Francisco. He has been obsessed with understanding Deep Learning ever since he began learning about it in late 2016 and has been writing and speaking about it whenever he can ever since. Professionally, he has applied a variety of machine learning models in industry, taught data science to individuals and companies, and works on modeling and Python projects on the side. Full time, he teaches data science to companies via the Corporate Training team at Metis. He strives to find the simplicity on the other side of complexity.
"About this title" may belong to another edition of this title.
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